In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. ![]() The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. ![]() The past few decades have seen the progression and application of machine learning in diverse medical fields. Machine learning applications have momentously enhanced the quality of human life.
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